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Journal of the American Statistical Association Volume 104, 2009 - Issue 487
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Theory and Methods

Jackknife Empirical Likelihood

Bing-Yi Jing Bing-Yi Jing is Professor, Department of Mathematics, Hong Kong University of Science and Technology, Hong Kong . Junqing Yuan obtained her Ph.D. degree from Hong Kong University of Science and Technology . Wang Zhou is Associate Professor, Department of Statistics and Applied Probability, National University of Singapore, Singapore 117546 . Bing-Yi Jing’s research was partially supported by Hong Kong RGC grants HKUST6011/07P and HKUST6015/08P. Wang Zhou’s research was partially supported by grant R-155-000-083-112 at National University of Singapore. The authors would like to thank the Editor, an Associate Editor, and two referees for their very helpful comments and criticisms which have helped improve the paper a great deal.
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Junqing Yuan Bing-Yi Jing is Professor, Department of Mathematics, Hong Kong University of Science and Technology, Hong Kong . Junqing Yuan obtained her Ph.D. degree from Hong Kong University of Science and Technology . Wang Zhou is Associate Professor, Department of Statistics and Applied Probability, National University of Singapore, Singapore 117546 . Bing-Yi Jing’s research was partially supported by Hong Kong RGC grants HKUST6011/07P and HKUST6015/08P. Wang Zhou’s research was partially supported by grant R-155-000-083-112 at National University of Singapore. The authors would like to thank the Editor, an Associate Editor, and two referees for their very helpful comments and criticisms which have helped improve the paper a great deal.
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Wang Zhou Bing-Yi Jing is Professor, Department of Mathematics, Hong Kong University of Science and Technology, Hong Kong . Junqing Yuan obtained her Ph.D. degree from Hong Kong University of Science and Technology . Wang Zhou is Associate Professor, Department of Statistics and Applied Probability, National University of Singapore, Singapore 117546 . Bing-Yi Jing’s research was partially supported by Hong Kong RGC grants HKUST6011/07P and HKUST6015/08P. Wang Zhou’s research was partially supported by grant R-155-000-083-112 at National University of Singapore. The authors would like to thank the Editor, an Associate Editor, and two referees for their very helpful comments and criticisms which have helped improve the paper a great deal.
Pages 1224-1232 | Received 01 May 2008, Published online: 01 Jan 2012
 
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Abstract

Empirical likelihood has been found very useful in many different occasions. However, when applied directly to some more complicated statistics such as U-statistics, it runs into serious computational difficulties. In this paper, we introduce a so-called jackknife empirical likelihood (JEL) method. The new method is extremely simple to use in practice. In particular, the JEL is shown to be very effective in handling one and two-sample U-statistics. The JEL can be potentially useful for other nonlinear statistics.

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